Iron quantification of brain microbleeds

ABSTRACT

An automatic method for detection and quantification of localized sources of magnetic susceptibility in images using a computer with instructions for a parameter module; an image input and filtering module; a second image filtering module; and a pattern quantification module.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation-in-Part of U.S. patentapplication Ser. No. 13/107,799, titled “Iron Quantification of BrainMicrobleeds,” filed May 13, 2011, which claims the benefit of U.S.Provisional Patent Application No. 61/334,522, titled “IronQuantification of Microbleeds in Postmortem Brain,” filed May 13, 2010,the contents of which are incorporated by reference herein in theirentirety.

BACKGROUND

Brain microbleeds (BMB) are associated with ischemic and hemorrhagicstroke, cerebral amyloid angiopathy (CAA), neurotrauma, Alzheimer'sdisease (AD), vascular dementia, cognitive decline, hypertension andaging. The presence of BMB in ischemic stroke, intracerebral hemorrhage(ICH) and CAA is associated with future hemorrhage. Whether presence ofBMB increases the risk of bleeding when thrombolytic and antithromboticagents are used is an important and controversial question. Thus, BMBare associated with both chronic and acute illness of no smallconsequence in our aging population.

BMB are visible in gradient recalled echo (GRE) T₂* magnetic resonance(MR) imaging as focal regions of signal loss and have beenhistopathologically related to hemosiderin, the (paramagnetic)iron-protein complex associated with pathologic iron storage followinghemorrhage and ferritin breakdown. Thus, BMB represent a source ofpathologic iron in the brain that is potentially cytotoxic (e.g., byfree radical production through the Fenton reaction). Oxidative damage,iron accumulation and/or changes in iron metabolism have been implicatedin neurodegenerative and cerebrovascular diseases. In addition, sinceiron is deposited at the site of a BMB in proportion to the amount ofextravasated blood, iron content in BMB can be considered a marker forthe severity of underlying vessel disease. Therefore the quantified ironcontent in BMB is potentially informative regarding disease progressionand the efficacy of treatment.

Past efforts to quantify brain iron have focused on content estimationwithin distributed brain regions. BMB, however, represent a localizedsource of iron deposition. Iron content and concentration of BMB havebeen heretofore absent in the literature. In addition, conventional“magnitude” MR images have significant limitations especially forlocalized iron quantification, and the well known blooming effecttypically obscures the true dimensions of an iron susceptibility source.A few studies have compared radiologic BMB to postmortem human tissueand have noted evidence of associated tissue damage. However, in vivomethods would allow the investigation of temporal relationshipsregarding tissue damage evolution following BMB and possibleinterventions. In particular, methods correlating BMB iron contentlevels with the severity and evolution of tissue damage can shed lighton the role of iron in the disease process.

Therefore, there is the need for an improved method of quantifying ironin BMB, which is capable of determining iron content and/orconcentration, that is not associated with disadvantages, like theblooming effect, of conventional “magnitude” MR images.

SUMMARY

According to one embodiment of the invention, there is provided a methodto quantify localized iron sources using MR phase images. This methodcan be used to quantify iron content and estimate true source diameter,unobscured by the blooming effect, in actual BMB.

According to one embodiment of the invention, there is provided a methodto quantify iron content or effective diameter of a localized ironsource in an anatomical region of a subject, comprising the steps of (a)obtaining a phase image from a magnetic resonance scan of the anatomicalregion of the subject; (b) identifying a dipole pattern in the phaseimage corresponding to a localized iron source in the anatomical region;(c) measuring one or more than one image parameter of the dipolepattern; and (d) relating the image parameter measured in step (c) to aquantity of the iron contained in the localized iron source or theeffective diameter of the source.

In a preferred embodiment of the invention, the phase image is obtainedfrom an in vivo magnetic resonance scan.

In alternative versions of the method, the phase image is a raw phaseimage, a high-pass filtered phase image or phase-enhanced magnitudeimage.

In a particularly preferred embodiment of the method, the anatomicalregion comprises a portion of a brain and the localized iron sourcecorresponds to a brain microbleed.

According to one embodiment of the invention, the subject is a mammal,preferably a human.

In alternative versions of the method, the dipole pattern is in ahorizontal, coronal or axial orientation.

According to one embodiment of the invention, the dipole pattern isidentified using one or more than one matching template.

In one embodiment, the image parameters r_(π) and r′_(π) are determinedfrom a rectangle bounding the dipole pattern, the rectangle comprising awidth and a height, where r_(π) is one half of the width and r′_(π) isone half of the height of the bounding rectangle.

Another embodiment of the present invention further comprises convertinggray-scale high pass filtered phase images to binary images beforedrawing bounding rectangles.

In alternative versions of the method, the image parameter is related tothe mass of iron, the iron concentration or the diameter of thelocalized iron source.

Another embodiment of the present invention further comprisescategorizing disease severity based on the quantity of iron determinedin step (d).

One embodiment of the present invention provides a method that furthercomprises repeating steps (a), (b), (c) and (d) at a later time point tomonitor any change in disease severity.

The invention is described in more detail by the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the presentinvention will become better understood with regard to the followingdescription, appended claims, and accompanying figures where:

FIG. 1A shows a bounding rectangle drawn around a schematic of aspherical dipole pattern in horizontal orientation, where the main fieldB₀ is parallel to the axis of the dipole, the black lines represent thephase wraps (shown are π and 3π wraps), and the widths and heights ofthe rectangle are respectively 2r_(π) and 2r_(π);

FIG. 1B shows a bounding rectangle drawn around the dipole in axialorientation showing π and 3π phase wraps, where the plane shown is across section of the equatorial plane of FIG. 1A, thus the dimensionsare equal to 2r_(π) and the main field is shown projecting out of theplane of the page;

FIG. 1C shows phase profiles drawn along the dipole equator (dottedlines in FIGS. 1A and 1B);

FIG. 1D is a magnified view of the wrapped profile in FIG. 1Csuperimposed with a circle representing a spherical iron source;

FIG. 2A is an MR image showing tissue slices of postmortem brainembedded in agarose and scanned at high field MRI, where an arrow pointsto a cortical brain microbleed (BMB);

FIG. 2B shows a T1 weighted image of the tissue slices of postmortembrain shown in FIG. 2A that reveals the presence of the BMB;

FIG. 2C is a close-up view of the tissue BMB shown in FIG. 2A (top ofpanel) and the corresponding MR image shown in FIG. 2B (bottom ofpanel);

FIG. 2D is an MR image showing small pieces of tissue containing BMB(arrow) or free of BMB (cut from dotted region) that were dissected andassayed for iron content using atomic absorption spectroscopy;

FIG. 3A shows magnitude images of two cortical grey BMB in postmortemCAA brain in horizontal orientations, exhibiting characteristic dipolepatterns;

FIG. 3B shows raw phase images of two cortical grey BMB in postmortemCAA brain in horizontal orientations, exhibiting characteristic dipolepatterns;

FIG. 3C shows high-pass filtered phase images of two cortical grey BMBin postmortem CAA brain in horizontal orientations, exhibitingcharacteristic dipole patterns, where the top lesion (dotted rectangle)is shown in the axial images (FIGS. 3E-3H);

FIG. 3D shows phase-enhanced magnitude images of two cortical grey BMBin postmortem CAA brain in horizontal orientations, exhibitingcharacteristic dipole patterns;

FIG. 3E shows a magnitude image of a cortical grey BMB (the top lesionin FIG. 3C) in postmortem CAA brain in axial orientation;

FIG. 3F shows a raw phase image of a cortical grey BMB (the top lesionin FIG. 3C) in postmortem CAA brain in axial orientation;

FIG. 3G shows a high-pass filtered phase of SWI image of a cortical greyBMB (the top lesion in FIG. 3C) in postmortem CAA brain in axialorientation;

FIG. 3H shows a phase-enhanced magnitude SWI image of a cortical greyBMB (the top lesion in FIG. 3C) in postmortem CAA brain in axialorientation;

FIG. 3I shows a bounding rectangle surrounding a magnification (12×) ofthe top lesion in the panel of FIG. 3C (dotted rectangle) in horizontalorientation;

FIG. 3J shows a bounding rectangle surrounding a magnification (12×) ofthe top lesion in the panel of FIG. 3C (dotted rectangle) in axialorientation (FIG. 3J is a magnification of the panel shown in FIG. 3G);

FIG. 3K is a magnification of the lower (unmarked) lesion in the panelshown in FIG. 3C showing the π phase wrap and an interior hyperintensering of radius≦r_(3π) (respectively, highlighted by dotted lines);

FIG. 3L is the same image as FIG. 3K showing a dotted rectangle used toestimate the diameter of the inner ring (dotted lines);

FIG. 4 is a graph showing BMB Iron Content vs. Image Parameters;

FIG. 5 is a graph showing estimated BMB lesion diameters;

FIG. 6 is a scatter plot showing estimated BMB iron concentrations for13 samples;

FIG. 7A shows two successive coronal sections of a rat brain with acollagenase-induced bleed in the right caudate/putamen (CP);

FIG. 7B shows dissected tissue surrounding the bleed taken from theright CP and a control tissue sample taken from the left CP;

FIG. 8A is a phase enhanced magnitude image of in vivo rat brain showinga well formed dipole;

FIG. 8B is a HP image of the dipole shown in FIG. 8A, where the dipolephase wraps are well defined and a bounding rectangle (defined in thebinary image of FIG. 8C) is shown superimposed on the HP image;

FIG. 8C is binary conversion of FIG. 8B showing distinct black/whitephase wraps delineated by a bounding rectangle;

FIG. 8D shows a phase enhanced magnitude image of a less distinctdipole;

FIG. 8E is an HP image of the dipole shown in FIG. 8D, where the bottomvertical phase wrap is indistinct and a bounding rectangle (defined inthe binary image of FIG. 8F) is shown superimposed on the HP image

FIG. 8F is binary conversion of FIG. 8E showing distinct black/whitephase wraps delineated by a bounding rectangle;

FIG. 9A shows a magnitude SWI image in coronal orientation of a BMBinduced in the caudate/putamen of the living rat brain;

FIG. 9B shows a raw phase SWI image in coronal orientation of a BMBinduced in the caudate/putamen of the living rat brain;

FIG. 9C shows a high pass-filtered SWI image in coronal orientation of aBMB induced in the caudate/putamen of the living rat brain;

FIG. 9D shows a phase-enhanced magnitude SWI image in coronalorientation of a BMB induced in the caudate/putamen of the living ratbrain;

FIG. 9E shows a magnification of the dipole pattern in FIG. 9C;

FIG. 9F shows magnitude SWI images in axial orientation of a BMB inducedin the caudate/putamen of the living rat brain (two successive slicesare shown);

FIG. 9G shows raw phase SWI images in axial orientation of a BMB inducedin the caudate/putamen of the living rat brain (two successive slicesare shown);

FIG. 9H shows high pass-filtered SWI images axial orientation of a BMBinduced in the caudate/putamen of the living rat brain (two successiveslices are shown);

FIG. 9I shows phase-enhanced magnitude SWI images in axial orientationof a BMB induced in the caudate/putamen of the living rat brain (twosuccessive slices are shown);

FIG. 10 is a graph showing the cube root of iron mass fromcollagenase-induced BMB plotted against r′_(π) and

FIG. 11 is a flowchart of some steps of a method for automaticquantification of localized sources of magnetic susceptibility.

DETAILED DESCRIPTION

According to one embodiment of the invention, there is provided a methodto quantify iron content of a localized iron source in an anatomicalregion of a subject. The method comprises the steps of (a) obtaining aphase image from a magnetic resonance scan of the anatomical region ofthe subject; (b) identifying a dipole pattern in the phase imagecorresponding to a localized iron source in the anatomical region; (c)measuring one or more than one image parameter of the dipole pattern;and (d) relating the image parameter measured in step (c) to a quantityof the iron contained in the localized iron source.

Methods and devices that implement the embodiments of the variousfeatures of the invention will now be described with reference to thedrawings. The drawings and the associated descriptions are provided toillustrate embodiments of the invention and not to limit the scope ofthe invention. Reference in the specification to “one embodiment” or “anembodiment” is intended to indicate that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least an embodiment of the invention. The appearancesof the phrase “in one embodiment” or “an embodiment” in various placesin the specification are not necessarily all referring to the sameembodiment.

As used in this disclosure, except where the context requires otherwise,the term “comprise” and variations of the term, such as “comprising”,“comprises” and “comprised” are not intended to exclude other additives,components, integers or steps.

In the following description, specific details are given to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. Well-known circuits,structures and techniques may not be shown in detail in order not toobscure the embodiments. For example, circuits may be shown in blockdiagrams in order not to obscure the embodiments in unnecessary detail.

Also, it is noted that the embodiments may be described as a processthat is depicted as a flowchart, a flow diagram, a structure diagram, ora block diagram. Although a flowchart may describe the operations as asequential process, many of the operations can be performed in parallel,distributed or concurrently. In addition, the order of the operationsmay be rearranged. A process is terminated when its operations arecompleted. A process may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

Moreover, a storage may represent one or more devices for storing data,including read-only memory (ROM), random access memory (RAM), magneticdisk storage mediums, optical storage mediums, flash memory devicesand/or other machine readable mediums for storing information. The term“machine readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, wireless channels andvarious other mediums capable of storing, containing or carryinginstruction(s) and/or data.

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, or a combination thereof. Whenimplemented in software, firmware, middleware or microcode, the programcode or code segments to perform the necessary tasks may be stored in amachine-readable medium such as a storage medium or other storage(s).One or more than one processor may perform the necessary tasks inseries, concurrently or in parallel. A code segment may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or a combination of instructions,data structures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing and/or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted through a suitable means including memory sharing,message passing, token passing, network transmission, etc.

In the following description, certain terminology is used to describecertain features of one or more embodiments of the invention.

The term “portable electronic device” refers to any current or futureelectronic device such as a smartphone, a table, an eBook reader or thelike that displays information to one or more than one user.

Various embodiments provide a device and a method for a remote controlfor portable electronic devices that is simple operate and operable witha single hand. One embodiment of the present invention provides a systemcomprising one or more portable electronic device and one or more remotecontrol for portable electronic devices. In another embodiment, there isprovided a method for using the system. The system and method will nowbe disclosed in detail.

LIST OF SYMBOLS r′_(π) point where the phase reaches the value of πalong vertical dipole axis U Collagenase enzyme activity units Δ_(χ)change in magnetic susceptibility ρ density m mass m_(Fe) mass of ironf_((Fe)) w/w iron concentration POD Postoperative day

Theoretical Background and Rationale

MR voxels containing and surrounding paramagnetic (or ferromagnetic)brain iron deposits have an altered local field ΔB, and thus an alteredmagnetization phase with respect to their neighbors. This phasedifference is detectible in GRE pulse sequences and described by theformula (for a right handed system):Δφ=−γΔBT _(E)  [1]where Δφ is the change in phase, γ is the proton gyromagnetic ratio, andTE is the echo time. Thus, the amount of iron in a voxel can potentiallybe related to the phase.

An easily identified parameter in modulo 2π phase-wrapped images can bemathematically related to iron mass in a localized spherical sample (Eq.2). Briefly, FIG. 1A shows a schematic cross section of an iron sampledipole phase pattern induced by the main MRI magnetic field (B₀), andFIG. 1C shows a corresponding phase profile taken across the horizontaldotted line in FIG. 1A. The value on the abscissa corresponding toΔφ_(D)=π is denoted by r_(π) and r_(π) is easily measured from thewrapped profile (FIG. 1D) or the rectangle bounding the π-phase wrap ofthe dipole pattern (FIG. 1A). Under the assumptions that i) the magneticsusceptibility is constant both internal (χ_(i)) and external (χ_(e)) tothe iron sample, ii) Δχ, the susceptibility difference, defined asΔχ≡(χ_(i)−χ_(e)), is very small (˜10⁻⁵), iii) the density of the samplesp is constant, and iv) all background phase has been removed, r_(π) isrelated to iron mass of the sample by Eq. 2:

$\begin{matrix}{m_{Fe} = {\left( \frac{4\pi^{2}\rho}{\;{\mathcal{V}\;\Delta\;\mathcal{X}\; B_{0}T_{E}}} \right)r_{\pi}^{3}}} & \lbrack 2\rbrack\end{matrix}$

The true radius a of the iron source (unobscured by the blooming effect)can also be related to r_(π) by Eq. 3, and for a given Δχ, r_(π) can inprinciple be converted among magnet field strength and echo time by Eq.4:

$\begin{matrix}{a = {\left( \frac{3\pi}{\mathcal{V}\;\Delta\;\mathcal{X}\; B_{o}T_{E}} \right)^{1/3}r_{\pi}}} & \lbrack 3\rbrack \\{\frac{r_{\pi_{2}}}{r_{\pi_{1}}} = \left( \frac{B_{0_{2}}T_{E_{2}}}{B_{0_{1}}T_{E_{1}}} \right)^{1/3}} & \lbrack 4\rbrack\end{matrix}$

In this present study, we denote by r′_(π) the value on the ordinatedipole axis corresponding to Δφ_(D)=π. Thus, r′_(π) is analogouslyrelated to the vertical n-wrap and phase profile (taken along verticaldotted line in FIG. 1A) as r_(π) is to the horizontal. It follows thatthe profile intensities are proportional to each other (proportionalityconstant of −2), and that r_(π) and r′_(π) are related by Eq. 5 below.Thus, r′_(π) analogs of Eqs. 2-4 can be expressed as Eqs. 6-8:r′ _(π)=2^(1/3) r _(π)

$\begin{matrix}{m_{Fe} = {\left( \frac{2\pi^{2}\rho}{\mathcal{V}\;\Delta\;\mathcal{X}\; B_{0}T_{E}} \right)r_{\pi}^{\prime\; 3}}} & \lbrack 6\rbrack \\{a = {\left( \frac{3\pi}{2\mathcal{V}\;\Delta\;\mathcal{X}\; B_{o}T_{E}} \right)^{1/3}r_{\pi}^{\prime}}} & \lbrack 7\rbrack \\{\frac{r_{\pi_{2}}^{\prime}}{r_{\pi_{1}}^{\prime}} = \left( \frac{B_{0_{2}}T_{E_{2}}}{B_{0_{1}}T_{E_{1}}} \right)^{1/3}} & \lbrack 8\rbrack\end{matrix}$

Both r_(π) and r′_(π) can simultaneously be measured from the dimensionsof the rectangle bounding the dipole phase pattern as shown in FIG. 1A.Because r′_(π) is larger than r_(π) (Eq. 5) it has the advantage of alarger dynamic range compared with r_(π).

FIG. 1B shows an axial cross section of the phase-wrapped dipole patterncorresponding to the equatorial plane of FIG. 1A. The prominent curvedlines in FIGS. 1A and 1B correspond to phase wraps (π and 3π wraps areshown) which appear as concentric circles in the axial orientation for aspherical source. Thus, r_(π) can alternately be determined from axialbounding rectangles (FIG. 1B), and the horizontal profile (along thehorizontal dashed line of FIG. 1A and shown in FIGS. 1C and 1D) isequivalent to an axial profile along any diameter (e.g., along thehorizontal dashed line of FIG. 1B). Careful examination of the profilesreveal that ideally, phase wraps can be used to determine the diameterof a susceptibility source (FIGS. 1C and 1D). In addition, while Eqs. 3and 7 require a knowledge of Δ_(χ) to relate source diameter to r_(π)and r′_(π) in principle diameter can be determined by phase wrapgeometry alone. In practice, signal loss in noisy dipole centers willobscure higher order phase wraps. Nevertheless, the sequence of phasediameters 2r_(π), 2r_(3π), 2r_(5π), etc. that are discernable provideincreasingly better approximations of the true diameter, and allowsclassification of sources based on diameter thresholds unobscured byblooming. It follows from equation 4 that r_(nπ)=n^((−1/3))r_(π). Thisimplies that higher order phase diameters need not be measured directly.If a higher order phase wrap is present, the corresponding phasediameter can be calculated based on the most robust wrapping diameters,r_(π) or r′_(π).

Eqs. 2 and 6 predict proportional relationships between the r_(π) andr′_(π) parameters and sample iron mass. Therefore, plots of theseexperimentally measured variables were used to verify the predictionsand validate the method. Assuming similar values of iron sample densityand susceptibility, such plots can then be used as standard curves topredict iron content in similar samples. In addition, we use phasediameters to estimate BMB source diameter and iron concentration.

FIGS. 1A-1D are schematics and drawings of an Image ParameterMeasurement that shows phase image parameters can be measured usingbounding rectangles or phase profiles. FIG. 1A shows a boundingrectangle drawn around schematic of a spherical dipole pattern inhorizontal. orientation. The widths and heights of the rectangle (solidlines) are respectively 2r_(π) and 2r′_(π). The curved lines representthe phase wraps (shown are π and 3π wraps). The main field B₀ isparallel to the axis of the dipole. FIG. 1B shows a bounding rectangledrawn around the dipole in axial orientation showing π and 3π phasewraps. The plane shown is a cross section of the equatorial plane ofFIG. 1A, thus the dimensions are equal to 2r_(π). The main field isshown projecting out of the plane of the page. FIG. 1C shows phaseprofiles drawn along the dipole equator (dotted lines in FIGS. 1A and1B) appear modulo 2π (lower trace). These wrapped profiles can be usedto measure r_(π), or can in principle be unwrapped (upper trace) andused to estimate, d, the diameter of the iron source or an arbitraryphase value (e.g., 3π). The unwrapped profile intersects the wrappedprofile where the phase Δφ_(D) is equal to π. The parameter r′_(π) canbe related to the phase profile taken along the vertical black dottedline in FIG. 1A. FIG. 1D is a magnified view of the wrapped profile inFIG. 1C superimposed with a circle representing a spherical iron source.Peak to peak widths of phase wrappings are proportional to r_(π) andr_(3π). Note that the distance between the most medial peaks is equalthe diameter of the source (d). However, since these peaks do notrepresent a full phase wrapping d<2r_(5π), and r_(3π)<d<r_(5π).

To test magnetic resonance phase image methods to quantify iron contentand source diameter of brain microbleeds (BMB) in postmortem humantissue. Tissue slices containing BMB were imaged using a susceptibilityweighted imaging protocol at 11.7 T. Image features and parameters inhigh-pass filtered phase images were related to lesion iron content andsource diameter by using a mathematical model. BMB lesions weredissected from tissue slices and assayed for iron using atomicabsorption spectrometry. BMB iron content was plotted against phaseimage parameters to compare experimental data with underlying theory.Image features and model equations were used to estimate BMB diameterand iron concentration. A strong linear relationship predicted by theorywas observed in the experimental data, validating our methods, andpresenting a tentative standardization curve where BMB iron in similartissues could be related to image parameters.

Postmortem Human BMB Sample Preparation

Post mortem human brain tissue was donated from the Alzheimer's DiseaseResearch Center Brain Bank at the University of California, Los Angeles.The research protocol was approved by the Institutional Review Board ofLoma Linda University Medical Center. On average five 1 cm coronalslices representing frontal, temporal/parietal and occipital lobar areaswere obtained from three cases histopathologically diagnosed as comorbidfor advanced AD (Braak and Braak V-VI) and CAA (Vonsattel stage 3). Thetissue slabs were embedded in 2% agarose and imaged on a Siemens' 3 TMRI clinical scanner using a standard SWI protocol (referred to in textas preparation images). After imaging, the tissue was separated from theagarose and magnitude and phase preparation images were used to identifyand dissect microbleeds. Approximately 40 blocks of tissue werecollected, each containing at least one BMB. Care was taken to representeach autopsy case and each brain region. To alleviate air-tissueinterface susceptibility artifacts encountered in ex vivo imaging ofbrain tissue, tissue blocks were again embedded in 2% degassed agarosein plastic scintillation tubes. Care was taken to eliminate small airbubbles which produce artifacts that can mimic the presence of BMB iron.

FIGS. 2A-2D are MR images of BMB in Postmortem Brain. FIG. 2A is an MRimage showing tissue slices were embedded in agarose and scanned at highfield MRI. Arrows point to a cortical BMB and FIG. 2B shows a T1weighted image that reveals the presence of the BMB (11.7 T, TR/TE:630.8/17.9, NEX: 4, FOV: 22 mm, MAT: 256×256, Thk: 0.3 mm). FIG. 2C is aclose-up view of the tissue BMB (top of panel) and the corresponding MRimage correlate (bottom of panel). FIG. 2D is an MR image showing smallpieces of tissue containing BMB (arrow) or free of BMB (cut from dottedregion) that were dissected and assayed for iron content using atomicabsorption spectroscopy.

Magnetic Resonance Imaging

Susceptibility weighted imaging (SWI) is a GRE sequence that usesmagnetic susceptibility-dependent complex phase information to provideor enhance image contrast and is very sensitive in BMB detection.Besides the preparation image sequence used in sample preparation (seeabove), we used two SWI sequences for this study (referred to in text asdata images): a 3D SWI horizontal sequence, and a 2D axial sequence. The2D sequence was used because comparative 3D axial scans had significantbackground phase apparently due to inadequate magnetic shimming.

Sample tubes were scanned in an 11.7 T small vertical-bore MR scanner(Bruker Biospin, Billerica Mass.) using the following parameters: 1): 3Dhorizontal sequence: TR/TE: 100/7 ms, flip angle: 20°, matrix: 256×256,NEX: 1, FOV: 2.2 cm, in-plane resolution: 0.0859 mm×0.0859 mm, and 32slices of thickness 0.688 mm. 2) a 2D SWI axial sequence: TR/TE:154.4-617/7 ms, flip angle: 20°, matrix: 256×256, NEX: 4, FOV: 2.2 cm,in-plane resolution: 0.0859 mm×0.0859 mm, slices: 20-40 of thickness0.688 mm.

Image and Data Processing

Raw horizontal and axial phase images were respectively high-passfiltered with 16×32 and 32×32 frequency domain filters, respectivelyusing SPIN software (MRI Institute, Detroit, Mich.). Magnitude imageswere multiplied four times by a phase mask created from the high-passfiltered (HP) images to produce phase-enhanced magnitude images. Theimage parameters r_(π) and r′_(π) were obtained from the horizontal HPimages using the height and width of the rectangle bounding the dipolephase patterns of each sample. In addition, the ratio of the sides ofrectangles bounding dipole patterns in the axial images were used tohelp assess the spherical symmetry of the samples. Bounding rectanglesare shown in the schematics of FIGS. 1A and 1B, and for real datasamples in FIGS. 3I and 3J. Using the ImageJ software package (ImageJ,NIH), the rectangle was drawn, its height and width were determined, andr_(π)(r′_(π)) is calculated as ½ of width (height) of the rectangle.

Iron Content Determination

Tissue slices were removed from agarose and BMB were located in theslices with the aid of SWI data images. Small blocks of tissuesurrounding the BMB were dissected from the slices using a diamond knife(FIG. 2). To increase the fraction of BMB iron versus background iron,the surrounding tissue in each block was trimmed away as deemednecessary or practical. Control blocks were also dissected and care wastaken so that control blocks were cut out of similar tissue (e.g.,cortical grey matter) as BMB blocks. All sample blocks were weighed witha precision mass balance. Samples were wet ashed: blocks (2-21 mg) weredissolved in 250 μl of 70% HNO₃ for 12 to 48 hours, heated at 80° C. for20 min, and allowed to cool to room temperature. 250μl of 10M of H₂O₂were added, and after 30 min, samples were heated at 70° C. for 15minutes and allowed to cool. Iron concentrations were measured intriplicate by graphite furnace atomic absorption spectrometry (SpectrAA220Z, Varian, Victoria, Australia).

BMB Diameter and Iron Concentration Calculation

Bounding rectangles were drawn around the innermost hyperintense ring inaxial dipole patterns from 13 samples (FIG. 3K). The innermost ringcorresponded to the r_(3π) or r_(5π) phase wrap in the samples. In casesof uncertainty (5/13) the lower wrap was used (i.e., r_(3π) instead ofr_(5π)). The average of the dimensions of the rectangle was taken as thediameter of the lesion. Though the average ratio of the rectangledimensions was 0.96±0.03 (mean±standard error), the dimensions typicallydiffered by a few pixels (0 pixels—4 samples; 1 pixel—5 samples; 2pixels—3 samples; 3 pixels—1 sample), and rectangles were easier toobjectively place around the rings than circles. The phase diameters2r_(3π), 2r_(5π), and 2r_(7π) were calculated using the correspondingr_(π) for each sample according to the formulas: r_(nπ)=n^(−1/3)r_(π)for n=3, 5, and 7. Sample iron mass (as determined above) was divided by(π/6) d³ to obtain iron concentrations. Units of μg/cm³ were convertedto μg/g wet tissue weight assuming a tissue density equal to water.

Sample Inclusion/Exclusion

Over 40 putative BMB were originally identified in 40 MR scans. Forpractical reasons, a subset of 26 of the most promising putative BMBwere chosen and afterward underwent image and tissue processing.Selection was based primarily on the quality of the dipole appearance(e.g., symmetry, distinct edges) seen in magnitude SWI scans. Sevensamples were excluded after image and tissue processing: one sample wasdamaged during dissection, two samples displayed inadequate backgroundphase removal, one sample dipole was due to an air bubble, one sampledipole was highly distorted, and two samples displayed faint andindistinct dipoles. A total of 19 samples were included in the ironcontent analysis. Six additional samples were excluded from ironconcentration analysis and 13 samples were used.

Statistical Analysis

The predicted proportional relationships between BMB iron mass and theimage parameters r_(π) and r′_(π) was tested by linear regressionanalysis using SigmaPlot version 11 (Systat Software, Inc., Chicago,Ill.). Plots of these variables were constructed along with a best-fitleast squares regression lines. Normality of BMB iron concentration wastested using a Shapiro-Wilk test. Statistical significance wasconsidered at p<0.05.

Results

Magnitude, raw phase, high-pass filtered phase and phase-enhancedmagnitude horizontal and axial images of two BMB samples are shown inFIG. 3. Robust characteristic dipole patterns are seen in each image.High-pass filtered images generally exhibited dipole patterns withclearly visible phase wraps surrounded by a largely homogenousbackground and phase profiles with sharp peaks (FIGS. 3I-3K). Theaverage r′_(π) to r_(π) was 1.24±0.13 consistent with the theoreticalvalue of 2^(1/3)=1.26. This implies that the filtering did notsignificantly distort the aspect ratio of the dipole pattern.

FIGS. 3A-3L are SWI of BMB in Postmortem CAA Brain, namely, magnitude(FIGS. 3A and 3E), raw phase (FIGS. 3B and 3F), high-pass filtered phase(FIGS. 3C and 3G) and phase-enhanced magnitude (FIGS. 3D and 3H) imagesof two cortical grey BMB, showing horizontal (FIGS. 3A-3D) and axial(FIGS. 3E-3H) orientations respectively. The top lesion (dottedrectangle) in the panel of FIG. 3C is shown in the axial images (FIGS.3E-3H). Characteristic dipole patterns were seen in each image. In FIGS.3I and 3J bounding rectangles are shown surrounding magnifications (12×)of the top lesion in the panel of FIG. 3C (dotted rectangle) inhorizontal (FIG. 3I) and axial (FIG. 3J) orientations (FIG. 3J is amagnification of the panel shown in FIG. 3G). FIG. 3K is a magnificationof the lower (unmarked) lesion in the panel shown in FIG. 3C showing theπ phase wrap and an interior hyperintense ring of radius≦r_(3π)(respectively highlighted by red dotted lines). FIG. 3L is the sameimage as FIG. 3K showing a bounding rectangle used to estimate thediameter of the inner ring (dotted lines). The lesion diameter isestimated by this diameter, and bounded by the phase diameters 2r_(π)and 2r_(3π), (see text).

Measured iron mass, r′_(π), r_(π), as well as estimated diameter andiron concentration is shown for BMB samples in Table 1.

TABLE 1 Data from postmortem BMB. [Fe] Sample m_(Fe) (μg) r′_(π) (mm)r_(π) (mm) d (mm) (μg/g) 1 0.73 ± 0.05 0.86 ± 0.14 0.65 ± 0.09 — — 20.98 ± 0.02 0.99 ± 0.13 0.77 ± 0.09 1.031 1705 3 1.54 ± 0.03 1.03 ± 0.130.86 ± 0.09 1.203 1687 4 0.90 ± 0.02 0.86 ± 0.14 0.73 ± 0.09 0.988 17865 0.86 ± 0.04 0.95 ± 0.13 0.65 ± 0.09 0.859 2580 6 1.04 ± 0.03 1.20 ±0.12 0.86 ± 0.09 1.160 1269 7 2.01 ± 0.09 1.29 ± 0.12 0.99 ± 0.09 1.0313502 8 1.14 ± 0.07 1.07 ± 0.13 0.73 ± 0.09 — — 9 0.43 ± 0.06 0.86 ± 0.140.60 ± 0.09 0.816 1499 10 0.55 ± 0.03 0.77 ± 0.14 0.60 ± 0.09 0.859 165611 0.75 ± 0.03 1.03 ± 0.13 0.90 ± 0.09 0.988 1473 12 8.15 ± 0.16 1.81 ±0.12 1.50 ± 0.09 — — 13 0.49 ± 0.03 0.86 ± 0.14 0.77 ± 0.09 1.160 595 1413.1 ± 0.3  2.19 ± 0.11 2.02 ± 0.09 — — 15 0.065 ± 0.007 0.52 ± 0.180.43 ± 0.27 — — 16 1.02 ± 0.04 0.90 ± 0.14 0.86 ± 0.09 1.117 1400 172.50 ± 0.05 1.33 ± 0.12 1.12 ± 0.09 1.246 2464 18 3.79 ± 0.14 1.50 ±0.12 1.42 ± 0.09 1.461 2322 19 1.39 ± 0.10 1.16 ± 0.13 0.90 ± 0.09 — —Iron mass is measured by atomic absorption spectrometry. r′_(π) andr_(π) are measured by bounding rectangles in horizontal filtered phaseimages. Diameter and concentration estimates are determined as describedin the text.

BMB iron mass vs. r′_(π) ³ is plotted in FIG. 4, and exhibits a stronglinear relationship (R²=0.984, p<0.001) between variables with a slopeof 1260 μg/cm³ (p<0.001). The y-intercept of the plot is small butstatistically significant (−0.309 μg, p=0.021). This value is smallerthan all the iron masses of the samples except one (Sample 15 in Table1), and can be interpreted as an indicator of the sensitivity of thetechnique. The strong linear relationship is predicted by Eq. 6 and thusconfirms the usefulness of our quantification method to measure ironcontent in BMB from human tissues. BMB iron content ranged from 0.065 to13.1 μg (median 1.0).

FIG. 5 is a graph showing Estimated BMB Lesion Diameter Values.Estimated BMB lesion diameters are shown bounded by phase diameterestimates (2r_(π), 2r_(3π), 2r_(5π)) for 13 samples. The lesion diameterestimates are based on diameter estimates of inner hyperintense rings inaxial dipole phase patterns (FIGS. 3K and 3L). Phase diameters arecalculated from corresponding r_(π) values. BMB estimated diametersranged from 0.82 to 1.5 mm (median 1.0 mm). Diameter values ranged from0.82 to 1.5 mm (median 1.0 mm).

FIG. 6 is a scatter plot showing estimated BMB iron concentrations. Theiron concentration values were normally distributed with a mean value of1842 μg/g tissue. Ten of 13, and 12 of 13 samples fell within ±1, or +2standard deviations about the mean, respectively.

Discussion

The present study demonstrates that parameters from phase images can berelated to iron content in real BMB in postmortem tissue. As discussedbelow, FIG. 4 serves as at least a potential standard curve for humanBMB where r′_(π) values of filtered phase images can be related to BMBiron mass. The strong linear correlation is predicted by Eq. 6 andvalidates the quantification method, where the spherical dipole modelgives satisfactory results for our BMB samples. Iron sample massresolution from our tissue samples was estimated to be 0.3 μg based onthe statistically significant intercept (FIG. 4). In addition, we reportestimates of BMB iron mass and concentration based on direct tissue ironmeasurements and phase image parameters. We used atomic absorptionspectrometry (considered a gold standard for tissue metal concentrationmeasurements) to determine lesion iron content that ranged from 0.065 to13.1 μg with a median value of 1.0 μg. Lesion diameters were estimatedusing features of axial dipole phase patterns. The lesion ironconcentration was found to be normally distributed with a mean value of1842±202 μg/g (mean±standard error). Seventy-seven percent and 92% ofthe BMB sample iron concentrations fell within ±one and two standarddeviations of the mean respectively. A normal distribution allows for asimple assessment of disparity between e.g., BMB concentrations indifferent brain regions or diseases.

BMB are associated with a growing number of disease states and present asource of potentially cytotoxic iron to the brain in proportion to theextent of blood extravasation. Therefore the quantified iron content ofBMB is a potential valuable biomarker to monitor disease progression,treatment efficacy and risk factor assessments. In recent reports: thepresence of a single lobar bleed, or more than one lobar bleed fulfillin part the Boston criteria for the diagnosis of possible and probableCAA respectively; two or more baseline BMB is associated withprogression from MCI to outright dementia; BMB≧5 was associated withhigher risk of ICH than benefit of anti-thrombotic agents. Such studiesare examples where results and clinical implications are based on BMBnumber. However, measurement of iron content as a continuous variablegoes beyond an assessment of pathologic severity based onpresence/absence or a discrete number of bleeds. Our method of localizedBMB (iron) quantification allows the characterization of severity at thelevel of a single bleed, groups of bleeds, brain region or whole brain.For example, indices of iron load or disease burden could be defined ase.g. “the sum of the iron content for all lobar BMB”. Suchcharacterizations could provide advantages over diagnostic criteria,prognostic standards or therapeutic recommendations based on discretenumeric thresholds.

In efforts to improve interrater agreement in BMB detection and capturestandardized auxiliary information several investigators have developedsystematic BMB rating scales for reliable measures of presence, number,anatomical location, certain/uncertain status, and/or size. Results ofthe present study suggest that quantified iron content could enhance,the usefulness of such discrete data We found a wide range of ironcontent (0.065-13.1 μg) for BMB with estimated diameters ranging from0.82 to 1.5 mm. In addition, BMB with a diameter of 3.2 mm (unbloomed)and iron concentration of 2000 μg/g would contain ˜35 μg of iron.Therefore, since our mean concentration of 1842 μg/g is probably a lowerbound value, in BMB that typically meet rating scale inclusion criteria,iron content may range over two orders of magnitude. This suggests thatmere counts may not necessarily be i) a good indicator of bleedingseverity (e.g., 5 BMB of 1 μg each versus 1 BMB with 15 μg), or ii) asensitive means for patient and study group comparisons (e.g., “numberof BMB in the parietal cortex” versus “total iron load of parietalcortex”) compared to iron content per se.

It is well known that BMB hypointensity size seen in magnitude GRE T2*images are typically larger than the actual tissue lesion. This socalled blooming effect varies with field strength, scan parameters andmagnetic susceptibility of the source. Results from the present studydemonstrate that BMB diameter estimation unobscured by the bloomingeffect is possible in phase images. The Eqs. 4 and 8 further reveal thatthese determinations can in principle be effectively compared acrossvarious field strengths and echo times. With this new found ability, thebenefits of size criteria should be revisited. Indeed, for definitionsof iron load indices to be usefully compared between studies or clinicalsituations, BMB minimum and maximum size limits are necessary. Sourcedimension quantification in principle allows an objective definition ofsuch inclusion limits.

Automated BMB detection can further improve interrater agreement, aswell as increase clinical practicality. Results from the present studiesunderscore that the scale of such dipole templates can be related toiron content. Therefore, using appropriately scaled dipole templates(e.g., varying r′_(π) and r_(π) parameters) BMB can be, not only countedbut simultaneously their iron content can be quantified. Moreover, BMBcan also be classified by size using r_(π) based diameter estimates ordefinitions. As described above, estimates can be made from diameters ofcircles or rectangles best fit to inner phase wrap rings in axialfiltered phase images (FIGS. 3K and 3L). The corresponding phasediameters (as defined above) calculated from corresponding r_(π) valuescould provide upper and lower bound values to the diameter estimates.Alternately, diameter could be defined based purely on r_(π) values andused for classification. For example, lesion diameter can be defined asr_(5π) based on pertinent knowledge of source propertied. Finally, ironor disease load indices could then be calculated from count, locationand severity (i.e., iron content) data, and clinically or biologicallyrelevant threshold criteria could flag for further investigation,provide diagnostic information or therapeutic recommendations.

There are several limitations to the present study. First of all, whilepossessing several advantages over magnitude images for ironquantification, phase image approaches also face limitations. Phasecontrast depends on source geometry and orientation with respect to themain magnetic field, and field perturbations extend beyond sources ofsusceptibility and alter contrast of surrounding tissue. These effectsultimately arise from fundamental physical properties of the magneticfield (e.g., solenoidality) and cannot be fully eliminated. However,unlike quantification of iron in distributed tissue regions (e.g., rednucleus), our localized method is not impacted by such effects andactually exploits them: The r_(π) and r′_(π) image parameters arerelated to magnetic field intensity on the directionally dependentdipole pattern outside the localized susceptibility source. Advantage isconferred by 1) providing additional dynamic range in pixilated imagesallowing resolution of very small sources that could not otherwise beresolved. This range can be increased by increasing echo time providedthat the concomitant loss in SNR is not too large. 2) Since parametermeasurements occur far away from the actual lesion, the shape of thesource is less important. Under appropriate circumstances, this permitsthe assumption of a spherically symmetric source and greatly simplifiesquantification and clinical practicality.

Secondly, we have assumed that BMB iron sources are effectively spheres.In most BMB, the actual geometry can likely be ignored because of farfield effects. Indeed, this may be reflected in the definition of BMB as“round” hypointense GRE T₂*(magnitude) image features by recentlyproposed BMB rating scale. However, other recommendations allow for“ovoid” hypointensities and it is expected that the spherical geometryassumption may not be globally applicable. However, based on at leastthree observations, non-spherical effects do not seem to be important inthe present study: 1) The average ratio of the π-wrap bounding rectanglein the axial images was 0.99+0.08 suggesting circularity in theequatorial plane. 2) The r′_(π)/r_(π) ratio for the horizontal dipolepatterns was consistent with the theoretical value for a sphericalsource. 3) Under the spherical assumption, the plot of m_(Fe) vs. r′_(π)was strongly linear (FIG. 6).

Thirdly, our method is a linear model based on the assumptions ofuniform iron density and susceptibility. In the present study, the verygood linear correlation between iron mass and r′_(π) seems to imply thatthese BMB have a relatively small variance. However, because our BMBrepresent a small number of cases and a specific disease population, itis not possible to draw conclusions about BMB in other disease states.Therefore, the plot of m_(Fe) vs. r′_(π) (FIG. 4) can only be regardedas a tentative standardization curve of iron content.

Fourth, because sample inclusion before data processing and sampleexclusion after processing was based on dipole quality, our results arepossibly biased toward best-case scenarios. However, our study analyzedBMB in postmortem tissue slices and in almost all cases, lesions werevisible on the surface of the tissue. BMB dipoles in intact brain willnot be influenced by issues and artifacts associated with lesionbisection and cut-tissue interfaces. Therefore, dipoles of comparableiron content could likely be better formed than those excluded in ourstudy. In addition, faint excluded dipoles were associated with ironvalues at or below the sensitivity threshold of the method (˜0.3 μg),and thus do not actually contribute to a bias. Finally, background phasedistortion is less of an issue in clinical scanners when compared to theexperimental hardware used in the present study. In any case, whether ina clinical or experimental context, malformed and ambiguous phase imagefeatures are likely to be present. How often these cases occur and towhat extent the method is affected must be informed by further research.Finally, in such cases, automated image processing software could stillpossibly discern essential features from dipole patterns that appearfaint and indistinct to the human eye.

Finally, the present study was conducted in postmortem tissue with verysmall fields of view (2.2 cm) and at very high field 11.7 T. Eq. 8implies in principle that equivalent r_(π) and r′_(π) values areachievable even with the clinically state of the art 3.0 T magnets. Forexample, at 3.0 T an echo time of 27 ms is required to get dipoles withthe same r_(π) values as this current study (Eq. 8), a value well withincurrent use in BMB detection. However, practical issues concerningquestions of adequate SNR and sufficiently short scan times have yet tobe addressed with acquisitions using FOVs that are an order of magnitudelarger than in the current study.

In summary, these post mortem results demonstrate that real BMB can beaccurately related to prominent phase image parameters under the simpleassumption that BMB iron sources are spherically shaped. Our m_(Fe) vs.r′_(π) ³ plot can tentatively be regarded as a standard curve, allowingBMB iron content estimates in tissue states similar to our AD/CAAautopsy cases. In addition, phase image features were used to estimateupper bounds of BMB iron source diameters and lower bounds of ironconcentrations. Our method potentially allows the definition of ironload or disease burden indices at the level of a single bleed, to thewhole brain (e.g. “the sum of the iron content of all lobar BMB”), aswell as the classification of BMB by size unobscured by the bloomingeffect. A “count, classify and quantify” method can potentially be fullyor semi-automated, and results can in principle be compared across fieldstrengths and echo times. Such information can enhance prognostic anddiagnostic criteria in the context of cerebral vessel disease associatedlate onset dementias, as well as inform treatment decisions regardingthe use of thrombolytic or thrombotic agents.

In the following study, we applied our methods to small hemorrhagiclesions induced in the in vivo rat brain using bacterial collagenase. Asexpected by theory, measurements of geometric features in phase imagescorrelated with lesion iron content measured by graphite furnace atomicabsorption spectrometry. Iron content estimation following BMB in vivocan shed light on the role and temporal evolution of iron-mediatedtissue damage and the efficacy of potential treatments incerebrovascular diseases associated with BMB.

Animal Procedures

Our animal protocol was approved by the Loma Linda UniversityInstitutional Animal Care and Use Committee. Ten male Sprague-Dawleyrats (450-500 g) were placed under anesthesia (isoflurane) andpositioned in a stereotactic frame (Knopf Instruments, Tujunga, Calif.).A midline incision was made and the scalp was retracted. A small burrhole (˜1.5 mm) was drilled into the skull 0.5 mm anterior and 3.1±0.2 mmlateral from bregma. A single dose of type VII bacterial collagenase insaline (Sigma-Aldrich, St. Louis, Mo.) was injected 6.1 mm below theskull surface using a Hamilton syringe (Reno, Nev.) placed in amicrosyringe pump (Model 310 Plus Series, KD Scientific, Holliston,Mass.) targeting the caudate/putamen (CP) of the rat brain. A range ofcollagenase doses (0.11 U/200 nl saline, 0.14 U/200 nl, 0.15 U/200 nl,0.16 U/200 nl, 0.17 U/200 nl, 0.18 U/200 nl, 0.2 U/200 nl, 0.22 U/200nl, 0.24 U/200 nl saline) were injected over 5 min. To minimize bleedingof injectate up the needle tract, the needle was held in place for 20minutes following the injections and then slowly withdrawn atapproximately 0.5 mm/min. The burr hole was filled with bone wax, thescalp sutured shut, and the rats were allowed to recover. One animal waseuthanized on postoperative day (POD) 2 due to surgical complicationsand 9 rats underwent MR scanning.

MR Imaging

Rats were scanned POD 28±2 in a 4.7 T small animal MR scanner (BrukerBiospin, Billerica Mass.) using two SWI sequences with the followingparameters: 1) 3D coronal SWI: TR/TE: 46.5/25 ms (TR/TE: 39/20 ms fortwo rats), flip angle: 17°, matrix: 256×256, NEX: 3, FOV: 3.0 cm,in-plane resolution: 117 μm×117 μm, and 32 slices of thickness 0.938 mm.2) 2D axial SWI: TR/TE: 558.3/25 ms (TR/TE: 1248.8/20 ms for two rats),flip angle: 200, matrix: 256×256, NEX: 6, FOV: 3.0 cm, in-planeresolution: 117 μm×117 μm, slices: 10, 12 or 32 of thickness 0.8 mm.

Lesion Dissection

Immediately following MR scanning, animals were sacrificed byexsanguination by way of transcardial perfusion with 4% bufferedparaformaldehyde (PFA). Brains were extracted from the skull and fixedin 4% PFA. The rat brains were sectioned coronally anterior andposterior to the needle tract that was visible at the top of the brain.Additional dissection was performed as necessary to reveal theanterior/posterior lesion boundaries and trim away excess tissue.Sections of tissue surrounding the lesion (ipsilateral CP) and controlsections (contralateral CP) were dissected out of the coronal slicesusing a diamond knife and nylon and titanium forceps (FIG. 7) andweighed on a precision balance.

FIG. 7A shows two successive coronal sections of a rat brain with acollagenase-induced bleed in the right CP. FIG. 7B shows dissectedtissue surrounding the bleed taken from the right CP, and a controltissue sample taken from the left CP.

Iron Content Determination

Samples were wet ashed: blocks (20-70 mg) were dissolved in 250μ1 of 70%HNO₃ overnight, heated at 80° C. for 20 min, and allowed to cool to roomtemperature. 250 μl of 10M of H₂O₂ were added and, after 30 min, sampleswere heated to 70° C. for 15 min and allowed to cool. Ironconcentrations were measured in triplicate by graphite furnace atomicabsorption spectrometry (GF-AAS) (SpectrAA 220Z, Varian, Victoria,Australia). Assuming that contra- and ipsilateral CP have comparablebackground iron concentrations, lesion iron content was calculated usingthe formula: m_((Fe)L)=f_((Fe)I)m_(I)−f_((Fe)C)m_(I), where m_((Fe)L) isthe mass of iron in the lesion, f_((Fe)I)(f_((Fe)C)) is the w/w Feconcentration of the ipsilateral (contralateral) tissue block; and m_(I)is the mass of ipsilateral tissue.

Image Processing

3D (2D) raw phase images were high-pass filtered using a 16×32 (32×32)frequency domain filter (39) using SPIN software (SPIN software, MRIInstitute, Detroit, Mich.). Magnitude images were multiplied four timesby the product of the negative and positive phase mask of reference. Theimage parameter r′_(π) was obtained using the Vertical dimension ofrectangles bounding the coronal phase dipole patterns in the 3D images.However, all gray-scale images were converted to binary images usingImageJ software (NIH) before bounding rectangles were drawn allowing amore objective determination of r′_(π) in one case where the location ofthe vertical phase wrap was ambiguous (see FIG. 8). Still, a slightambiguity remained for Case 3 (reflected in the larger uncertainty forr_(π)′ in Table 2 (see Appendix A)). Measured r′_(π) values from imagesacquired with T_(E)=20 ms were scaled by a factor of 1.08 in accordancewith Eq. 8 of (38). (Note that 2D images are used for lesionvisualization only (FIG. 8))

FIGS. 8A-8F show the assignment of bounding rectangles in binary imagesof in vivo rat brain. All high pass filtered phase (HP) images wereconverted to binary images before bounding rectangles were drawnallowing a more objective determination of r′_(π) for one sample. FIGS.8A and 8D are phase enhanced magnitude images, FIGS. 8B and 8E are HPimages, FIGS. 8C and 8F are binary conversions of FIGS. 8B and 8E fromtwo different cases. In FIGS. 8A and 8B the dipole phase wraps are welldefined, whereas the bottom vertical phase wrap in FIG. 8E isindistinct. However, the binary images (FIGS. 8C and 8F), show distinctblack/white phase wraps for both samples. The rectangles defined inbinary images FIGS. 8C and 8F are shown superimposed on the HP images inFIGS. 8B and 8E.

Statistical Analysis

The relationship predicted by Eq. 6 between BMB iron mass and r′_(π) wastested by linear regression analysis using SigmaPlot version 11 (SystatSoftware, Inc., Chicago, Ill.). A plot of (m_(Fe))^(1/3) vs. r′_(π) wasconstructed along with a best-fit least squares regression line.Statistical significance was considered at p<0.05. Finally, we note thatthe independent variable in the regression model is measured withuncertainty. In general, uncertainty in a predictor variable leads tobias in the regression parameters. However, in the present study theslope and intercept are free from bias, and coefficient of determination(R²) is attenuated by <4% (see Appendix B). We report the attenuatedvalue.

Error Analysis

Uncertainties in the plotted variables (m_(Fe))^(1/3) and r′_(π) aregiven by Eqs. 9-13 (see Appendix A for more details):δr _(π)′=(s ² δn ₊ ² +δn ⁻ ² +t ²/36²)^(1/2)  [9]δr_(π)′ is the uncertainty in r_(π)′, s is the in-plane voxel dimensionparallel to B₀, t is the slice thickness, and δn+ and δn− arerespectively the uncertainty in number of voxels of the upper and loweredges of the bounding rectangle.

$\begin{matrix}{{\delta\left( m_{Fe}^{1/3} \right)} = \frac{\delta\; m_{Fe}}{3m_{Fe}^{2/3}}} & \lbrack 10\rbrack\end{matrix}$δm _(Fe)=((δm _(I))²(f _(Fe)I) −f _((Fe)C))² +m _(I) ²((δf_((Fe)I))²+(δf _((Fe)C))²))^(1/2)  [11]

$\begin{matrix}{{\delta\; f_{{({Fe})}I}} = {\left( \frac{\alpha}{m_{I}} \right)\left( {{\left( {m_{I} + m_{s}} \right)^{2}\left( {STE}_{f_{{({Fe})}I}} \right)^{2}} + \left( {0.5\mspace{11mu} f_{{({Fe})}I}} \right)^{2} + \left( {\delta\; m_{I}} \right)^{2}} \right)^{1/2}}} & \lbrack 12\rbrack \\{{\delta\; f_{{({Fe})}C}} = {\left( \frac{\alpha}{m_{C}} \right)\left( {{\left( {m_{C} + m_{s}} \right)^{2}\left( {STE}_{f_{{({Fe})}C}} \right)^{2}} + \left( {0.5\mspace{11mu} f_{{({Fe})}\; C}} \right)^{2} + \left( {\delta\; m_{C}} \right)^{2}} \right)^{1/2}}} & \left\lbrack 513 \right.\end{matrix}$δ(m_(Fe) ^(1/3)) and δm_(Fe) are the uncertainty in m_(Fe) ^(1/3) andm_(Fe) respectively, α is a dilution factor and m_(s) is the mass ofHNO₃ solution (both required in the GF-AAS iron assay), STE stands forstandard error, δm_(I) and δm_(C) are respectively the uncertainty inipsilateral and contralateral tissue block mass, and all other variablesare as defined in the Iron Content Determination section above.

The uncertainty in the predicted iron mass, δ{circumflex over (m)}_(F),given by Eq. 14 below, is a different kind of uncertainty that can beused to assess how measurement errors in r_(π)′ affect how well thelinear model compares with the experimental data (see Appendix A):

$\begin{matrix}{{\delta\;{\hat{m}}_{F_{e}}} = {\left( \frac{6\;\pi^{2}\rho}{\mathcal{V}\;\Delta\;\mathcal{X}\; B_{o}T_{E}} \right)r_{\pi}^{\prime\; 2}\delta\; r_{\pi}^{\prime}}} & \lbrack 14\rbrack\end{matrix}$

Results

Consistent with right CP lesions, right circling behavior, left forelimbparesis, and loss of left whisker reflex was observed in rats in varyingdegree and duration following surgery. MR images confirmed the presenceof these lesions in the CP (FIGS. 9A-9I). Dipole patterns were observedin SWI phase, filtered phase, phase mask, phase-enhanced magnitude, andto a lesser extent, magnitude images. However, when compared to ourprevious focal quantification studies, the dipole patterns weregenerally not as distinct nor as symmetric. The lesions were generallymore spatially distributed and not spherical, typically elongated in adorsal-ventral (e.g., see FIG. 7 and FIGS. 9F-9I) and rostral-caudalorientations. One rat was excluded because the lesion dipole pattern wasdistorted by background phase and image data from 8 animals wereanalyzed. Iron mass and image parameter data for these animals are shownin Table 2. The median iron mass of the induced BMB was 1.40 μg.

TABLE 2 Collagenase-Induced BMB Image Data Subject m_(Fe) (μg)(m_(Fe))^(1/3) (μg^(1/3)) r′_(π) (mm) 1 1.19 ± 0.02 1.06 ± 0.01 0.95 ±0.05 2 2.19 ± 0.04 1.30 ± 0.01 1.33 ± 0.05 3 0.76 ± 0.02 0.91 ± 0.010.88 ± 0.07 4 1.33 ± 0.06 1.10 ± 0.02 0.88 ± 0.05 5 1.47 ± 0.03 1.14 ±0.01 1.17 ± 0.05 6 1.19 ± 0.03 1.06 ± 0.01 1.17 ± 0.05 7 1.74 ± 0.031.20 ± 0.01 1.06 ± 0.05 8 2.41 ± 0.03 1.34 ± 0.01 1.64 ± 0.05 Data isshown for collagenase-induced lesions in the CP of the rat brain. Ironmass (mFe) was measured using atomic absorption spectrometry. r′π datawas obtained from the vertical dimension (i.e., parallel to B0) ofrectangles bounding the coronal dipole phase pattern in binary images.Uncertainties were calculated using Eqs. 9-13, A1 and A2 as described inthe text and Appendix A.

FIGS. 9A-9I show: in vivo axial and coronal SWI scans of collagenaseinduced BMB. In vivo magnitude (FIGS. 9A and 9F), raw phase (FIGS. 9Band 9G), high pass-filtered (FIGS. 9C and 9H), phase-enhanced magnitude(FIGS. 9D and 9I) SWI images in coronal (FIGS. 9A-9D) and axialorientations (FIGS. 9F-9I) of a BMB induced in the caudate/putamen ofthe living rat brain (subject 1, Table 2) (two successive slices areshown for axial images). FIG. 9E shows a magnification of the dipolepattern in FIG. 9C. Note the dorsal/ventral elongation of the bleeds inthe axial images.

FIG. 10 shows a graph where the cube root of iron mass fromcollagenase-induced BMB is plotted against r′_(π). The plot of m_(Fe)^(1/3) vs. r′_(π) revealed a statistically significant linearrelationship (attenuated R²=0.65, p=0.012) as was expected based on thetheory underlying the quantification method. The slope was found to be4.4 μg/g^(1/3)/cm (p=0.012) and the intercept, while predicted tovanish, was statistically different from zero (0.64 μg^(1/3), p=0.005).The effective mass magnetic susceptibility (Δ_(χ)/ρ) of the samplelegions was calculated from the slope and found to be 7.2×10⁻⁶ m³/kg,about 5 times larger than reports for human liver ferritin andhemosiderin and about 9 times larger than reported in our postmortemhuman study. Finally, using the slope of FIG. 10, δ{circumflex over(m)}_(F) of Eq. 5 was found to range from 0.01 to 0.04 μg, with a meanof 0.02 μg, a standard deviation of 0.01 μg, and a median of 0.02 μg.

Discussion

In the present work, we tested our focal iron quantification techniquein a simple collagenase-induced BMB model in the living rat brain. TheBMB dimensions were on the order of 1-2.5 millimeters in size andcontained 0.8 to 2.4 μg of iron. Thus, the size and iron content of theBMB were relevant to human BMB. Dipole patterns were present inexperimentally induced bleeds and were measured for the r′_(π)parameter. A plot of the cube root of lesion iron mass against r′_(π)produced a linear graph as expected by the theory underlying thequantification technique. This can be thought of as a standard curvewhereby other BMB could be assayed for iron content in the rat brain.

However, the dipole patterns associated with collagenase hemorrhageswere not as distinct as previously observed from BMB in postmortem humanbrain, nor was the linear correlation relating iron mass and r′_(π) asstrong (r=0.81 (attenuated) vs. r=0.99). This is most likely due to lessfocal bleeding in the present study (FIG. 1 and FIGS. 3F-3I). While anestablished ICH rat model typically delivers 0.2 U of collagenase in 1μl of saline, the smaller doses and volumes used in the present studyreflect an attempt to induce smaller and more focal hemorrhagic legionsto better model BMB. However, it appears the collagenase injectionscaused more spatially diffuse bleeding than is characteristic of realBMB which generally originate from a single ruptured vessel. Refinementsto the BMB induction technique leading to more concentrated lesions anddistinct dipoles are possible. For example, diffusion of collagenasethrough the brain tissue likely contributes to the non-localityincluding dorsal/ventral and rostral/caudal elongation of the inducedlesions. The use of a glass micro needle (which have outer diameters anorder of magnitude smaller than the 26 gauge needle used here) and theassociated smaller bore needle tract could lead to less diffusion ofenzyme away from the injection site and thus more localized bleeding andiron deposition. On the other hand, an alternate model that usesultrashort laser pulses to induce hemorrhages in targeted singlemicrovessels may offer superior results compared to a collagenaseinduced BMB model at least for cortical bleeds.

Critical to the validation of the quantification technique is thecorrelation between BMB iron measured by GF-AAS and the phase imageparameter r_(π)′. In the living rat brain, lesion susceptibility variesas red cell breakdown, heme degradation and hemosiderin formationevolves. Similarly, in this experiment, the amount of paramagnetic irondetectible by (or “visible” to) the MR scanner is also expected to varywith lesion age. Therefore since GF-AAS always measures the total ironpresent in the tissue, perfect correlations can only occur if theimaging is done when all the iron extravasated following the BMB isvisible. In humans, hematoidin, a marker of free iron release due tohemeoxygenase activity, peaks around POD 10 and hemosiderin depositionpeaks around POD 10-14. We chose POD 28 to image and sacrifice the ratsassuming that hemosiderin formation would be essentially complete.However, one possible explanation for the unexpected non-zero interceptin the plot of FIG. 10 is the presence of non-MR visible iron. Alongitudinal study design with 1) multiple time point extending beyondPOD 28, and 2) with an additional component investigating the magneticand biochemical properties of lesion iron and associated molecules inthe tissue, could shed further light on how lesion iron accumulation,concentration, magnetic properties and MR-visibility vary over time.

The non-zero intercept in FIG. 10 suggests that not all iron isaccounted for by r_(π)′ measurements. Examination of Table 2 and FIG. 10reveals that the mass of iron associated with point pairs with similaror equal r′_(π) values differ by ˜0.3-0.6 μg. This suggests that thesensitivity of the technique at this image resolution may alsocontribute to the non-zero intercept. However, the largest uncertaintyof predicted iron mass (m_(Fe hat)) for our 8 cases was found to be˜0.04 μg, over six times smaller than that implied by intercept of 0.64μg^(1/3). Therefore, discreet voxel size alone may not explain thediscrepancy. Besides a contribution from non-visible iron discussedabove, an alternate possibility is that the single r_(π)′ parameter onlyapproximates the iron content of non-spherical lesions. Finally, anotherpoint of interest consequent from FIG. 10 is the difference in lesionmass susceptibility compared with human liver hemosiderin and ourpostmortem study. Whether this results from differences in species,bleeding mode, artifacts of fixation on tissue and lesionsusceptibility, or other factors is an important question that calls forfurther study.

In conclusion, we have shown that an SWI phase image method can be usedto estimate focal iron content on the order of a few micrograms in theliving rodent brain using simple modifications of an establishedcollagenase ICH model. Improvements to the experimental technique or theuse of alternate bleeding models are expected to produce superiorresults and could further increase the usefulness of the technique inanimal models. This will allow investigation of the temporal progressionof bleeding lesions and the role of iron-mediated tissue damage and theefficacy of therapeutic interventions in small cerebral vessel diseaseassociated with BMB.

Method Description

Referring now to FIG. 11, there is shown a semi-automated softwaremethod is provided for the quantification of localized iron sources inbrain microbleeds (BMB) using magnetic resonance phase images. The ironsources are considered to be spherical iron deposits in the BMB. By“quantification of . . . iron” we mean i) quantification of the mass ofiron in the BMB, and ii) quantification of the “true” (i.e., apart forthe blooming effect) effective iron source diameter.

The quantification and analysis software system consists of the modulesbelow. As per common software engineering practice, these modules can beimplemented in object oriented code as base classes from which morespecialized variations of the modules can be derived (thus, while we usethe term ‘modules’ for simplicity, ‘family of modules’ is a betterdescription). The modules are organized into a “pipeline” framework thatsequentially processes the modules (in the order of there appearancebelow: Steps 1 through 8). However, only Steps 1, 2, 4 and 5 arerequired for a processing run and the remaining modules are optional.Finally, we note that the object-oriented framework design allows thesystem to be easily extended so that new functionality can be added asis deemed useful for specific applications of the method. We thereforediscuss the function or features of each module in general terms andthen offer specific examples of functionality. The modules are asfollows:

1. Parameter Module

The parameter module 1102 contains values for various parameters used toprocess all modules in the pipeline. The most important set ofparameters are the equation coefficients used to calculate iron massbased on image measurements. These are the coefficients in equation 6described above. These coefficients can be obtained using standardcurves of BMB iron vs. phase image parameters as described above. Otherparameters, for example, specify which specific calculation and analysismodules to use in process run. The user selects the parameters at thebeginning of processing from a graphical user interface (GUI).

2. Image Input and Filtering

A standard image library (e.g., ITK) is used to read a processed MRphase image stack into the pipeline framework 1104. The image stack canbe processed as i) a set of two dimensional (2D) images, or ii) mergedinto a single three dimensional (3D) volume. If the images do notcontain isotropic voxels, interpolation using standard methods must beperformed for case ii.

3. Image Filtering

Various filters can be applied to the image data 1106 before the patternidentification step. For example, an ‘image layer’ identifyinganatomical brain regions can be imported to or drawn on an image stack.

4. Pattern Identification

Dipole pattern identification 1108 is accomplished using variations ofthe 2D (case i, Step 2) or 3D (case ii, Step 2) PDQ (Phase mapcross-correlation Detection and Quantification) method described byMills et al. (Mills PH, Wu Y-JL, Ho C, Ahrens ET. Sensitive andautomated detection of iron-oxide-labeled cells using phase imagecross-correlation analysis. Magnetic Resonance Imaging 2008;26(5):618-628.), which is incorporated herein by reference in itsentirety. In this method the distinct dipole ‘burger’ shape is used as atemplate and systematically overlaid onto every template-sized area ofthe phase image (see e.g., FIGS. 1 and 3 above for examples of dipolepatterns in modeled or real BMB, and FIG. 2 of the Mills et al.reference for examples of 2D and 3D templates used by the PDQ method).The similarity between the template and image patch is given a scorebased on a cross-correlation formula. An essential variation of themethod for our application is instead of a single template, multipletemplates, each scaled according to r′_(π) are passed over the image andscored. Because the right-sized template will have the highest score, animage feature (i.e., a putative dipole pattern) is matched based notonly on mere presence of a dipole, but also on iron content. A secondvariation is the use of circular template for axial oriented images. Theradius of this template is scaled by various parameters: d*, r_(π),r_(3π), r_(5π), etc., that can be used to estimate the value or range ofthe effective iron source diameter (unobscured by the ‘bloomingeffect’). The degree of matching is scored using a numerical scale wherezero represents no correlation to a particular template and 100 is aperfect match—that is the image feature and the dipole template areidentical. Score threshold values are used to decide if the imagefeature is identified as a dipole pattern within an acceptabletolerance. In a borderline case, the feature is determined a ‘possible’dipole pattern and is flagged for manual inspection. For example, ascore of 80 or greater could be considered a dipole and a score between50 and 55 a possible dipole. Once a feature is identified as a dipole, arecord is created in a database that stores information about thefeature.

5. Pattern Quantification

The scale of such dipole templates (e.g., quantified by r_(π)′, d*, orbounded by r_(3π), etc.) can be related to iron content and effectiveiron source diameter as described above. In the Pattern Identificationmodule 1108 each of the dipole pattern templates is associated with theproper r_(π)′ and/or d*, etc. In this Pattern Quantification module1110, an image feature is assigned an iron content level, effectivediameter, upper-bound diameter, and/or diameter range (depending on theapplication), based on the template that best matches the image feature.In other words, e.g. the r_(π)′ value from the template with the largestscore is assigned to the image feature and then the iron content iscalculated based on r_(π)′ using equations and parameters in theParameter module 1102. Relevant quantification information is added tothe database for the image feature.

6. Feature Classification

After image features are automatically identified and quantified, theycan be classified 1112 based on what the system “knows” about theidentified features: what is in the database and Parameter module 1102data structures, and what is communicated through instantiated objectsof specific Feature Classification 1112 classes. For example, ifanatomical regions are mapped to an image stack through an Image Filterobject (as described in Step 3 above), a Feature Classification instancecan be used to automatically classify identified image features by e.g.,brain lobe or nucleus, and associate an appropriate “tag” with thefeature. For example, a location tag such as “Parietal Lobe” can beassociated with a feature identified in the parietal lobe of the brain.All classification information and tags are stored in the database.

a) Object Classification

After image features are automatically identified and quantified, objectclassification proceeds on two levels: 1) Automated classifications aredone based on what the system ‘knows’ about the identified features:what is in the database and Parameter module 1102 data structures, andwhat is communicated through instantiated objects of specific ObjectClassification classes. For example, if anatomical regions are mapped toan image stack through an Image Filter object (as described in Step 3above), an Object Classification instance can be used to automaticallyclassify identified image features by e.g., brain lobe or nucleus. 2)Information can also be supplied by the user. For example, followingStep 5 the image stack, marked with each identified feature, can bepresented to the user. The user can then manually associate the featurewith any number of tags. For example, a location tag such as “ParietalLobe” can be associated with feature identified in the parietal lobe ofthe brain. All tags and automatic classifications are stored in thedatabase.

7. Calculations and Analysis

This family of modules 1114 is used to perform calculations and analysisusing information stored in the database. For example, the system can bequeried to add the total iron content of all BMB in the parietal lobe,or calculate the average diameter of all the BMB in posterior brainregions. Similar information could potentially be valuable if it can beshown to be biologically or clinically relevant. Such queries can beinitiated automatically in the pipeline, or manually from the GUI at theend of processing.

8. Decision/Interpretation

Based on output from the Calculations and Analysis modules 1116 variousconditional criteria can be used to output a decision, interpretation orconclusion. For example, if the total iron content in a certain brainlocation falls between two particular threshold values, an output stringof “Moderate Iron Extravasation” could be assigned and displayed in theGUI. Alternately, an event could be triggered that would send an emailalert informing medical staff of a condition that requires furtherattention. Ultimately, if validated by future clinical research, onecould hope that tentative diagnostic information or therapeuticguidelines could be suggested by a future implementation of this module.

In another embodiment of the present invention there is presented anautomated method for quantification of localized sources of magneticsusceptibility. The method can be used in various image types and indifferent problem domains. What is essential is a dipole pattern thatrepresents a magnetic field induced in a material by an externalmagnetic field that can be imaged and analyzed. What is described hereinis an automated method in the specific context of quantifying ironcontent and effective iron source diameter in phase images of BMB as anexample of how the method can be used. However, other localized sourcesof paramagnetic or diamagnetic material may similarly be quantified inother applications (e.g., medical, industrial, etc). Therefore, thisexample is not meant to be limited only to BMB or medical imaging aswill be appreciated by those with skill in the art with reference tothis disclosure.

This example of the present invention demonstrates an automated methodfor quantification of localized iron sources in brain microbleeds (BMB)using magnetic resonance phase images. The iron sources are generallyspherical iron deposits in the BMB. The term “quantification of . . .iron” means: i) quantification of the mass of iron in the BMB, and ii)quantification of the “true” effective iron source diameter. Thebackground for the method for quantification can be found in:Quantification of punctate iron sources using magnetic resonance phase,Magn Reson Med 2010 63(1): 106-115; Iron quantification of microbleedsin postmortem brain, Magn Reson Med 2011; 65(6): 1592-601; and In vivoIron Quantification in Collagenase-induced Microbleeds in Rat Brain,Magn Reson Med 2011; June 30, each of which are incorporated byreference in their entirety.

The method can be implemented in object oriented code as base classesthat more specialized variations of the modules can be derived. Theobject-oriented framework design provides flexibility so that the methodcan be easily extended to add new functionality as will be understood bythose with skill in the art with reference to this disclosure. Thereforethe function or features of each module are described in general termsand specific examples of functionality but are not meant to limit otherpossible variations of the method.

Referring now to FIG. 10, there is shown a flowchart of an automatedmethod for detection and quantification of localized sources of magneticsusceptibility in images. As can be seen, the method comprises computerinstructions retrievable from a storage and executable on a processorfor executing a parameter module, an image input and filtering module, apattern identification, and a pattern quantification module. Optionally,the method can comprise a pattern quantification module, a featureclassification module, a calculations and analysis module, and adecision/interpretation module.

Parameter Module

The parameter module comprises instructions for holding global valuesand parameters used in all the modules. The most important set ofparameters are the equation coefficients used to calculate iron massbased on image measurements. These are the coefficients in equation 6from Iron quantification of microbleeds in postmortem brain. Thesecoefficients can be obtained using standard curves of BMB iron versusphase image parameters. Other parameters can specify a specificcalculation and optionally one or more than one analysis module to usein processing the provided data. A user sets the parameters at thebeginning of processing using a graphical user interface (GUI).

Image Input and Filtering Module

A standard image library (e.g., ITK) is used to read a processed MRphase image stack into the pipeline framework 1104. The image stack canbe processed as: i) a set of two dimensional (2D) images, or ii) mergedinto a single three dimensional (3D) volume. If the images do notcontain isotropic voxels, interpolation using standard methods must beperformed for case ii.

Image Filtering Module

Various filters can be applied to the image data before step 4. Forexample, an “image layer” identifying anatomical brain regions can beimported or drawn on an image stack.

Pattern Identification

Dipole pattern identification is accomplished using variations of the 2Dor 3D Phase map cross-correlation Detection and Quantification (PDQ)described in Sensitive and automated detection of iron-oxide-labeledcells using phase image cross-correlation analysis, Magnetic ResonanceImaging 2008; 26(5):618-628, which is incorporated by reference in itsentirety. A distinct dipole “burger” shape is used as a template andsystematically overlaid onto every template-sized patch of the phaseimage (see Figs 1 and 3 from Quantification of punctate iron sourcesusing magnetic resonance phase and Figs 1 and 3 from Iron quantificationof microbleeds in postmortem brain for examples of dipole patterns inmodeled or real BMB, and Fig 2 of Sensitive and automated detection ofiron-oxide-labeled cells using phase image cross-correlation analysisfor examples of 2D and 3D templates). The system calculates a scorebased on a cross-correlation formula of similarity between the templateand image patch. In a preferred embodiment, an essential variation ofthis method is used. Unlike previous systems that used a singletemplate, the present invention uses multiple templates, each scaledaccording to rπ′. The multiple templates are passed over the image andscored. The template with the correct size will have the highest score.An image feature (i.e., a putative dipole pattern) is matched based notonly the presence of a dipole, but also on iron content. In anotherembodiment a circular template is used for axial oriented images. Theradius of this template is scaled by various parameters (d*, πr, r3π,r5π, etc.) that can be used to estimate the value or range of theeffective iron source diameter unobscured by any ‘blooming effect’present in the image. The degree of matching is scored using a numericalscale where zero represents no correlation to a particular template and100 is a perfect match—that is the image feature and the dipole templateare identical. If the score falls above a threshold value that is set bythe user in the parameter module, the feature is identified as a dipolepattern and assigned a quantification parameter (d*, rπ, etc.)associated with the template. If the score is found to be above thethreshold for more than one template, the template with the highestscore is chosen. In this way, the “best fit” quantification parameter isassigned to the feature. It is important to note that the choice of thethreshold value will affect the sensitivity and specificity of dipoleidentification and parameter assignment. Because different applicationsof the method might necessitate different e.g. sensitivities, the useris allowed to set the threshold before the analysis begins. However, athreshold that corresponds to an 80% sensitivity in the determination ofbest fit templates from a standard image set (distributed with thesoftware and containing various dipoles patterns) is used as a defaultvalue. Once a feature is identified as a dipole and associated with theappropriate parameter, a record is created in a database that storesinformation about the feature.

Pattern Quantification Module

As described in Quantification of punctate iron sources using magneticresonance phase, Magn Reson Med 2010; 63(1): 106-115; Ironquantification of microbleeds in postmortem brain, Magn Reson Med 2011;65(6):1592-601; and In vivo Iron Quantification in Collagenase-inducedMicrobleeds in Rat Brain, Magn Reson Med 2011; June 30, each of which isincorporated by reference in its entirety, the scaling dipole templates(e.g., quantified by rπ′, d*, or bounded by r3π, etc.) can be related toiron content and effective iron source diameter.

In the Pattern Identification module, each of the dipole patterntemplates is associated with the proper quantification parameter (rπ′and/or d*, etc). In this Pattern Quantification module, an image featureis assigned an iron content level, effective diameter, upper-bounddiameter, and/or diameter range (depending on the application), based onthis parameter. In other words, e.g. the rπ′ value from the best fittemplate is used, along with appropriate equations of and values in theParameter module, to calculate the iron content of the image feature.Relevant quantification information for the feature is then added to thedatabase.

The system can optionally comprise a pattern identification module, apattern quantification module, a calculations and analysis module and adecision/interpretation module. Although these modules are specific toBMB, other modules are possible for other image types and domains.

Feature Classification

After image features are automatically identified and quantified, theycan be classified based on what the system “knows” about the identifiedfeatures: what is in the database and Parameter module data structures,and what is communicated through instantiated objects of specificFeature Classification classes. For example, if anatomical regions aremapped to an image stack through an Image Filter object (as described inStep 3 above), a Feature Classification instance can be used toautomatically classify identified image features by e.g., brain lobe ornucleus, and associate an appropriate “tag” with the feature. Forexample, a location tag such as “Parietal Lobe” can be associated with afeature identified in the parietal lobe of the brain. All classificationinformation and tags are stored in the database.

Calculations and Analysis

This family of modules is used to perform calculations and analysesusing information stored in the database. For example, the system can beset to automatically add the total iron content of all BMB in theparietal lobe, or calculate the average diameter of all the BMB inposterior brain regions. Similar information could potentially bevaluable if it can be shown to be biologically or clinically relevant.

Decision/Interpretation

Based on output from the Calculations and Analysis modules variousconditional criteria can be used to output a decision, interpretation orconclusion. For example, if the total iron content in a certain brainlocation falls between two particular values, an output string of“Moderate Iron Extravasation” could be assigned and displayed in theGUI. Alternately, an event could be triggered that would send an emailalert informing medical staff of a condition that requires furtherattention. Ultimately, if validated by future clinical research, onecould hope that tentative diagnostic information or therapeuticguidelines could be suggested by an implementation of this module.

Extension to Other Magnetic Materials and Other Application Areas

The present invention has been discussed in considerable detail withreference to certain preferred embodiments, other embodiments arepossible. For example, we have described the semi-automated method inthe specific context of quantifying iron content and effective ironsource diameter in phase images of BMB. However, more generally, thesame method can be used for other localized sources of magneticsusceptibility, in other image types, and in different problem domains.What is essential is a dipole pattern that represents a magnetic fieldinduced in a material by an external magnetic field. Therefore,localized sources of paramagnetic or diamagnetic material may similarlybe quantified in other medical or industrial applications. Accordingly,the scope of the appended claims should not be limited to thedescription of preferred embodiments contained in this disclosure. Allreferences cited herein are incorporated by reference in their entirety.

What has been described is a new and improved system and method for aremote control for portable electronic devices that is simple operateand operable with a single hand, overcoming the limitations anddisadvantages inherent in the related art.

Although the present invention has been described with a degree ofparticularity, it is understood that the present disclosure has beenmade by way of example. As various changes could be made in the abovedescription without departing from the scope of the invention, it isintended that all matter contained in the above description or shown inthe accompanying drawings shall be illustrative and not used in alimiting sense.

APPENDIX A

First order uncertainties were determined using standard techniques.δr _(π)′=((sδn)² +t ²/36²)^(1/2)  [A1]δn=(δn ₊ ² +δn ⁻ ²)^(1/2)  [A2]δ(r _(π)′)³=3(r _(π)′)² δr _(π′)  [A3]The uncertainties in r_(π)′ and r_(π)′³ (δr₉₀ ′ and δr_(π)′³respectively) due to the discreet nature of the voxels are given by Eqs.A1-A3. All variables have previously been defined in the text, exceptδn, the uncertainty in the number of voxels in the bounding rectangle inthe direction parallel to B₀. The second term in Eq. A2 involves theslice thickness t. δn₊=δn⁻ was taken to be 0.5, except for one casewhere the lower phase wrap was slightly ambiguous and δn⁻ was taken as1.0. Eq. 9 follows from Eqs. A1-A2. The uncertainly in lesion iron mass(δm_(Fe) and δm_(Fe) ^(1/3)) was previously expressed by Eqs. 10-13 inthe text. δm_(I) and δm_(C) in these equations is related to theprecision of the mass balance used to determine tissue block mass.

The above uncertainties are associated with the measured and plottedvariables in this experiment. The uncertainty in the predicted iron massdue to measurement error in r_(π)′ expressed by Eq. 6 and Eq. A3.

Finally, we note that uncertainty in r_(π)′ due to white noise wasgenerally an order of magnitude lower than that due to discrete voxelsize in this study and was thus neglected.

APPENDIX B

Due to the finite voxel size in our experiment, the true r_(π)′ (whichwe denote as r_(π)′*) is unknown and we perform the regression using asurrogate variable—i.e., r′_(π)′. These variables are related by Eq. A4:r _(π)′*=r _(π)′±δr _(π)′*  [A4]where δr_(π)′* is the estimated uncertainty in the unobserved variable.δr′_(π) * is due to the finite voxel size and is independent of r_(π)′measurements, therefore Eq. A4 represents an unbiased Berkson errormodel. In such a model the regression coefficients are not biased, butthere is an increase in the residual variance, given by Eq. A5, and acorresponding decrease in R² seen in Eq. A6 (41,51):σ_(ε) ²→σ_(ε) ²+β²(δr _(π)′*)²  [A5]

$\begin{matrix}\left. R^{2}\rightarrow{R^{2} - \frac{{{\beta^{2}\left( {\delta\; r_{\pi}^{\prime}} \right.}{*)}}^{2}}{\sigma_{m^{1/3}}^{2}}} \right. & \left\lbrack {A\; 6} \right\rbrack\end{matrix}$Here σ_(ε) ² and σ_(m1/3) ² are the residual and total variancerespectively, β is model coefficient, and the second terms in Eqs. A4and A6 represent the increase and decrease due to error of the residualvariance and regression coefficient (R²), respectively. For the presentstudy, the last term of Eq. A6≈(4.4²)(0.005²)/(0.0195)=0.025. Thisrepresents a <4% decrease in from the naive R²=0.68 to 0.65 (theattenuated R²). We thus conclude that in the present study the slope andintercept are free from bias and the effect of error on the regressioncoefficient is small. Finally, we note that the attenuation depends onthe square of δr_(π)′ *. Thus, decreased in-plane resolution and slicethicknesses result in smaller attenuations (e.g.,δr_(π)′*→(0.5)(_(δ)r_(π)′*) leads to an attenuation of 0.01 or <2% inthe present case.

What is claimed is:
 1. A method for detection and quantification oflocalized sources of magnetic susceptibility in magnetic resonance scanimages obtained from a patient, the method comprising the steps of:subjecting a patient to scan by a magnetic resonance imaging device,whereby a phase image of an anatomical region of the patient isobtained; identifying a dipole pattern in the phase image correspondingto a localized iron source in the anatomical region; measuring one ormore than one image parameter of the dipole pattern, wherein the one ormore than one image parameter is related to a localized iron source;relating, using a computer, the one or more than one image parameterfrom the measuring to a quantity of iron contained in the localized ironsource or a concentration of iron in the localized iron source, whereinthe relating comprises: a) providing computer instructions for: 1) aparameter module comprising values for various parameters used in allmodules, where the values comprising equation coefficients are used tocalculate iron mass based on image measurements; 2) an image input andfiltering module comprising instructions from a standard image libraryto read a processed magnetic resonance scan into a storage for furtherprocessing; 3) a second image filtering module comprising instructionsfor identifying anatomical regions; 4) a pattern quantification modulecomprising instructions for scaling dipole templates; 5) a patternidentification module, where the pattern identification comprisesinstructions for dipole pattern identification; 6) a featureclassification module that comprises instructions to automaticallyidentify, quantify and classify image features; 7) a calculations andanalysis module, where the calculations and analysis module comprisesinstructions to perform calculations and analyses using informationstored in a database; and 8) a decision/interpretation module, where thedecision/interpretation module comprises instructions for analyzinginputs from the calculations and analysis modules to output a decision,interpretation or conclusion, and outputting, from the computer, thedecision, interpretation or conclusion.
 2. The method of claim 1, wherethe equation coefficients are derived using standard curves of brainmicrobleed iron versus phase image parameters.
 3. The method of claim 1,where the parameters comprise indicators of specific calculations andanalysis modules to use in the next steps of the method.
 4. The methodof claim 1, where the parameters are set by a user prior to processing.5. The method of claim 1, where the parameters are set by the user usinga graphical user interface.
 6. The method of claim 1, where theinstructions for the image input and filtering module further comprisescomputer instructions to process the image stack, the steps comprising:i) instructions for processing a set of two dimensional (2D) images; andii) instructions for merging the image stack into a single threedimensional volume.
 7. The method of claim 6, where the image input andfiltering module further comprises computer instructions for determiningif the image stack contains isotropic voxels.
 8. The method of claim 7,where the step of determining if the image stack contains isotropicvoxels for the instructions for merging the image stack into a singlethree dimensional volume comprise standard methods of interpolation. 9.The method of claim 1, where the instructions for the image filteringmodule can be executed prior to other steps of the method.
 10. Themethod of claim 1, where the instructions for the pattern quantificationmodule further comprises instructions for relating iron content andeffective iron source diameter to the scaled dipole templates.
 11. Themethod of claim 1, where the instructions for scaling dipole templatesare quantified by rπ′, d*, bounded by r3π, or any combination thereof.12. The method of claim 11, where the instructions for the patternidentification module associate each of the dipole pattern templateswith a quantification parameter.
 13. The method of claim 12, where animage feature is assigned from the group consisting of an iron contentlevel, an effective diameter, an upper-bound diameter, and a diameterrange.
 14. The method of claim 1, where the instructions for dipolepattern identification comprise instructions for using a templatecomprising a distinct dipole “burger” shape that is systematicallyoverlaid onto every template-sized patch of a phase image.
 15. Themethod of claim 1, where the similarity between the template and imagepatch is scored using a cross-correlation formula.
 16. The method ofclaim 1, where the similarity between the template and image patch isscored using a cross-correlation formula and multiple templates, eachscaled according to rπ′.
 17. The method of claim 1, where the similaritybetween the template and image patch is scored using a circular templatefor axial oriented images.
 18. The method of claim 17, where the radiusof the circular template is scaled by one or more than one parameters toestimate the value or range of the effective iron source diameterunobscured by the ‘blooming effect’.
 19. The method of claim 18, wherethe radius of the circular template is scaled by parameters selectedfrom the group consisting of d*, rπ, r3π and r5π.
 20. The method ofclaim 17, where the degree of matching is scored using a numericalscale, where zero represents no correlation to a particular template and100 represents that the image feature and the dipole template areidentical.
 21. The method of claim 20, further comprising the step ofdetermining if the score falls above a threshold value that is set bythe user in the parameter module.
 22. The method of claim 21, where ifthe score is found to be above the threshold for more than one template,the template with the highest score is chosen.
 23. The method of claim1, where the classification of image features is based on informationstored from the previous modules.
 24. The method of claim 23, where thestored information of the identified features is selected from the groupconsisting of parameter module data, parameter module data structures,and instantiated objects of specific feature classification classes. 25.The method of claim 1, where the calculations are selected from thegroup consisting of total iron content of all BMB in the parietal lobeand average diameter of all the BMB in posterior brain regions.
 26. Themethod of claim 1, where the output can be a string of “Moderate IronExtravasation” that is stored and displayed on a monitor.
 27. The methodof claim 1, where the output is an email alert informing medical staffof a condition that requires further attention.
 28. The method of claim1, wherein the phase image is obtained from an in vivo magneticresonance scan.
 29. The method of claim 1, wherein the phase image is araw phase image, a high-pass filtered phase image or phase-enhancedmagnitude image.
 30. The method of claim 1, wherein the dipole patternis in an orientation selected from the group consisting of a horizontalorientation, a coronal orientation, or an axial orientation.
 31. Themethod of claim 1, wherein the dipole pattern is identified using one ormore than one matching template.
 32. The method of claim 1, whereinimage parameters r_(π)and r′_(π)are determined from a rectangle boundingthe dipole pattern, the rectangle comprising a width and a height, wherer_(π)is one half of the width and r′_(π)is one half of the height of thebounding rectangle.
 33. The method of claim 32, further comprisingconverting gray-scale high pass filtered phase images to binary imagesbefore drawing bounding rectangles.
 34. The method of claim 1, whereinthe image parameter is related to the mass of iron, the ironconcentration or the diameter of the localized iron source.
 35. Themethod of claim 1, wherein the anatomical region is a brain, and whereinthe patient has a condition selected from the group consisting ofischemic stroke, hemorrhagic stroke, cerebral amyloid angiopathy,neurotrauma, Alzheimer' s disease, vascular dementia, cognitive decline,hypertension, and aging.
 36. The method of claim 1, wherein thelocalized iron source corresponds to a brain microbleed.
 37. The methodof claim 36, further comprising categorizing a condition severity basedon the quantity of iron determined.
 38. The method of claim 37, furthercomprising repeating steps of the method at a later time point tomonitor a change in the condition severity.